code: def params():
      return dict(
          # Additional parameters
          regressor_cols=None,
          # pmdarima fit parameters
          start_p=1,
          d=1,
          start_q=1,
          max_p=1,
          max_d=1,
          max_q=1,
          start_P=1,
          D=1,
          start_Q=1,
          max_P=1,
          max_D=1,
          max_Q=1,
          max_order=1,
          m=1,
          seasonal=True,
          stationary=False,
          information_criterion="bic",
          alpha=0.05,
          test="kpss",
          seasonal_test="ocsb",
          stepwise=True,
          n_jobs=1,
          start_params=None,
          trend=None,
          method="lbfgs",
          maxiter=50,
          offset_test_args=None,
          seasonal_test_args=None,
          suppress_warnings=True,
          error_action="trace",
          trace=False,
          random=False,
          random_state=None,
          n_fits=10,
          out_of_sample_size=0,
          scoring="mse",
          scoring_args=None,
          with_intercept="auto",
          # pmdarima predict parameters
          return_conf_int=True,
          dynamic=False,
      )
  
imports: import numpy
  import pandas
  import pytest
  from pmdarima.arima import AutoARIMA
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from testfixtures import LogCapture
  from greykite.common.constants import LOGGER_NAME
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import calc_pred_err
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.sklearn.estimator.auto_arima_estimator import AutoArimaEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: params() and returns: dict